兰州大学机构库 >信息科学与工程学院
Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
Yang, Y(杨裔)1; Shang, Zhihao2; Chen, Yao1; Chen, Yanhua3
2020
Source PublicationEnergies   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN19961073
EISSN1996-1073
Volume13Issue:3
page numbers19
AbstractAs energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.
© 2020 by the authors
Keywordelectric load forecasting extreme learning machine recurrent neural network support vector machines multi-objective particle swarm optimization algorithm
PublisherMDPI
DOI10.3390/en13030532
Indexed BySCIE ; EI
Language英语
Funding Project[2018YFB1003205]
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:000522489000026
PublisherMDPI AG, Postfach, Basel, CH-4005, Switzerland
EI Accession Number20200508095870
EI KeywordsElectric power plant loads ; Energy conservation ; Forecasting ; Knowledge acquisition ; Machine learning ; Multiobjective optimization ; Particle swarm optimization (PSO) ; Recurrent neural networks ; Support vector machines
EI Classification NumberEnergy Conservation:525.2 ; Electric Power Systems:706.1 ; Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4 ; Optimization Techniques:921.5
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/417612
Collection信息科学与工程学院
Corresponding AuthorShang, Zhihao
Affiliation
1.School of Information Science and Engineering, Lanzhou University, Lanzhou; 730000, China
2.Department of Mathematics and Computer Science, Free University of Berlin, Berlin; 14195, Germany
3.School of Information Engineering, Zhengzhou University, Zhengzhou; 450000, China
First Author AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Yang, Yi,Shang, Zhihao,Chen, Yao,et al. Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting[J]. Energies,2020,13(3).
APA Yang, Yi,Shang, Zhihao,Chen, Yao,&Chen, Yanhua.(2020).Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting.Energies,13(3).
MLA Yang, Yi,et al."Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting".Energies 13.3(2020).
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